Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations1200
Missing cells122
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory197.0 KiB
Average record size in memory168.1 B

Variable types

Numeric10
Categorical3
Boolean8

Alerts

Medical_History has 122 (10.2%) missing values Missing
ID is uniformly distributed Uniform
ID has unique values Unique
Panic_Attack_Frequency has 130 (10.8%) zeros Zeros
Caffeine_Intake has 199 (16.6%) zeros Zeros
Exercise_Frequency has 198 (16.5%) zeros Zeros
Alcohol_Consumption has 127 (10.6%) zeros Zeros

Reproduction

Analysis started2025-01-19 16:12:47.172939
Analysis finished2025-01-19 16:12:59.767379
Duration12.59 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Uniform  Unique 

Distinct1200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean600.5
Minimum1
Maximum1200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-19T19:12:59.886868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile60.95
Q1300.75
median600.5
Q3900.25
95-th percentile1140.05
Maximum1200
Range1199
Interquartile range (IQR)599.5

Descriptive statistics

Standard deviation346.55447
Coefficient of variation (CV)0.57710986
Kurtosis-1.2
Mean600.5
Median Absolute Deviation (MAD)300
Skewness0
Sum720600
Variance120100
MonotonicityStrictly increasing
2025-01-19T19:13:00.041135image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
807 1
 
0.1%
805 1
 
0.1%
804 1
 
0.1%
803 1
 
0.1%
802 1
 
0.1%
801 1
 
0.1%
800 1
 
0.1%
799 1
 
0.1%
798 1
 
0.1%
Other values (1190) 1190
99.2%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1200 1
0.1%
1199 1
0.1%
1198 1
0.1%
1197 1
0.1%
1196 1
0.1%
1195 1
0.1%
1194 1
0.1%
1193 1
0.1%
1192 1
0.1%
1191 1
0.1%

Age
Real number (ℝ)

Distinct47
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.134167
Minimum18
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-19T19:13:00.181024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q129
median42
Q353
95-th percentile62
Maximum64
Range46
Interquartile range (IQR)24

Descriptive statistics

Standard deviation13.543412
Coefficient of variation (CV)0.32924971
Kurtosis-1.1716845
Mean41.134167
Median Absolute Deviation (MAD)12
Skewness-0.059035943
Sum49361
Variance183.42402
MonotonicityNot monotonic
2025-01-19T19:13:00.325980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
43 40
 
3.3%
50 39
 
3.2%
64 34
 
2.8%
52 33
 
2.8%
54 33
 
2.8%
45 31
 
2.6%
22 31
 
2.6%
42 30
 
2.5%
19 29
 
2.4%
18 29
 
2.4%
Other values (37) 871
72.6%
ValueCountFrequency (%)
18 29
2.4%
19 29
2.4%
20 25
2.1%
21 23
1.9%
22 31
2.6%
23 27
2.2%
24 17
1.4%
25 29
2.4%
26 24
2.0%
27 22
1.8%
ValueCountFrequency (%)
64 34
2.8%
63 16
1.3%
62 28
2.3%
61 26
2.2%
60 17
1.4%
59 20
1.7%
58 21
1.8%
57 28
2.3%
56 28
2.3%
55 22
1.8%

Gender
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Female
549 
Male
537 
Non-binary
114 

Length

Max length10
Median length6
Mean length5.485
Min length4

Characters and Unicode

Total characters6582
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowFemale
4th rowMale
5th rowNon-binary

Common Values

ValueCountFrequency (%)
Female 549
45.8%
Male 537
44.8%
Non-binary 114
 
9.5%

Length

2025-01-19T19:13:00.471294image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-19T19:13:00.594985image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
female 549
45.8%
male 537
44.8%
non-binary 114
 
9.5%

Most occurring characters

ValueCountFrequency (%)
e 1635
24.8%
a 1200
18.2%
l 1086
16.5%
F 549
 
8.3%
m 549
 
8.3%
M 537
 
8.2%
n 228
 
3.5%
N 114
 
1.7%
o 114
 
1.7%
- 114
 
1.7%
Other values (4) 456
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6582
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1635
24.8%
a 1200
18.2%
l 1086
16.5%
F 549
 
8.3%
m 549
 
8.3%
M 537
 
8.2%
n 228
 
3.5%
N 114
 
1.7%
o 114
 
1.7%
- 114
 
1.7%
Other values (4) 456
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6582
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1635
24.8%
a 1200
18.2%
l 1086
16.5%
F 549
 
8.3%
m 549
 
8.3%
M 537
 
8.2%
n 228
 
3.5%
N 114
 
1.7%
o 114
 
1.7%
- 114
 
1.7%
Other values (4) 456
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6582
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1635
24.8%
a 1200
18.2%
l 1086
16.5%
F 549
 
8.3%
m 549
 
8.3%
M 537
 
8.2%
n 228
 
3.5%
N 114
 
1.7%
o 114
 
1.7%
- 114
 
1.7%
Other values (4) 456
 
6.9%

Panic_Attack_Frequency
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4125
Minimum0
Maximum9
Zeros130
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-19T19:13:00.695566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8476484
Coefficient of variation (CV)0.64535941
Kurtosis-1.2176754
Mean4.4125
Median Absolute Deviation (MAD)2
Skewness0.013703994
Sum5295
Variance8.1091013
MonotonicityNot monotonic
2025-01-19T19:13:00.812889image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 130
10.8%
2 130
10.8%
7 128
10.7%
8 127
10.6%
5 124
10.3%
4 122
10.2%
3 122
10.2%
1 110
9.2%
6 108
9.0%
9 99
8.2%
ValueCountFrequency (%)
0 130
10.8%
1 110
9.2%
2 130
10.8%
3 122
10.2%
4 122
10.2%
5 124
10.3%
6 108
9.0%
7 128
10.7%
8 127
10.6%
9 99
8.2%
ValueCountFrequency (%)
9 99
8.2%
8 127
10.6%
7 128
10.7%
6 108
9.0%
5 124
10.3%
4 122
10.2%
3 122
10.2%
2 130
10.8%
1 110
9.2%
0 130
10.8%

Duration_Minutes
Real number (ℝ)

Distinct40
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.3925
Minimum5
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-19T19:13:00.930214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q115
median24
Q334
95-th percentile43
Maximum44
Range39
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.39993
Coefficient of variation (CV)0.46735391
Kurtosis-1.1391063
Mean24.3925
Median Absolute Deviation (MAD)10
Skewness0.039993859
Sum29271
Variance129.95841
MonotonicityNot monotonic
2025-01-19T19:13:01.090287image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
21 40
 
3.3%
36 39
 
3.2%
44 39
 
3.2%
19 38
 
3.2%
17 37
 
3.1%
26 37
 
3.1%
32 34
 
2.8%
39 33
 
2.8%
42 33
 
2.8%
27 33
 
2.8%
Other values (30) 837
69.8%
ValueCountFrequency (%)
5 28
2.3%
6 28
2.3%
7 32
2.7%
8 29
2.4%
9 30
2.5%
10 29
2.4%
11 21
1.8%
12 32
2.7%
13 29
2.4%
14 32
2.7%
ValueCountFrequency (%)
44 39
3.2%
43 27
2.2%
42 33
2.8%
41 23
1.9%
40 21
1.8%
39 33
2.8%
38 22
1.8%
37 22
1.8%
36 39
3.2%
35 25
2.1%

Trigger
Categorical

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
Unknown
206 
PTSD
205 
Phobia
203 
Caffeine
202 
Social Anxiety
197 

Length

Max length14
Median length7
Mean length7.48
Min length4

Characters and Unicode

Total characters8976
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCaffeine
2nd rowStress
3rd rowPTSD
4th rowCaffeine
5th rowCaffeine

Common Values

ValueCountFrequency (%)
Unknown 206
17.2%
PTSD 205
17.1%
Phobia 203
16.9%
Caffeine 202
16.8%
Social Anxiety 197
16.4%
Stress 187
15.6%

Length

2025-01-19T19:13:01.218152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-19T19:13:01.350616image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
unknown 206
14.7%
ptsd 205
14.7%
phobia 203
14.5%
caffeine 202
14.5%
social 197
14.1%
anxiety 197
14.1%
stress 187
13.4%

Most occurring characters

ValueCountFrequency (%)
n 1017
 
11.3%
i 799
 
8.9%
e 788
 
8.8%
o 606
 
6.8%
a 602
 
6.7%
S 589
 
6.6%
P 408
 
4.5%
f 404
 
4.5%
t 384
 
4.3%
s 374
 
4.2%
Other values (15) 3005
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1017
 
11.3%
i 799
 
8.9%
e 788
 
8.8%
o 606
 
6.8%
a 602
 
6.7%
S 589
 
6.6%
P 408
 
4.5%
f 404
 
4.5%
t 384
 
4.3%
s 374
 
4.2%
Other values (15) 3005
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1017
 
11.3%
i 799
 
8.9%
e 788
 
8.8%
o 606
 
6.8%
a 602
 
6.7%
S 589
 
6.6%
P 408
 
4.5%
f 404
 
4.5%
t 384
 
4.3%
s 374
 
4.2%
Other values (15) 3005
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1017
 
11.3%
i 799
 
8.9%
e 788
 
8.8%
o 606
 
6.8%
a 602
 
6.7%
S 589
 
6.6%
P 408
 
4.5%
f 404
 
4.5%
t 384
 
4.3%
s 374
 
4.2%
Other values (15) 3005
33.5%

Heart_Rate
Real number (ℝ)

Distinct80
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.3025
Minimum80
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-19T19:13:01.506197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile83
Q1100
median121
Q3141
95-th percentile156
Maximum159
Range79
Interquartile range (IQR)41

Descriptive statistics

Standard deviation23.369912
Coefficient of variation (CV)0.19425957
Kurtosis-1.2119262
Mean120.3025
Median Absolute Deviation (MAD)21
Skewness-0.035392565
Sum144363
Variance546.15279
MonotonicityNot monotonic
2025-01-19T19:13:01.665989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 24
 
2.0%
150 22
 
1.8%
121 22
 
1.8%
96 22
 
1.8%
137 21
 
1.8%
128 21
 
1.8%
81 20
 
1.7%
89 20
 
1.7%
159 20
 
1.7%
148 19
 
1.6%
Other values (70) 989
82.4%
ValueCountFrequency (%)
80 17
1.4%
81 20
1.7%
82 16
1.3%
83 13
1.1%
84 11
0.9%
85 11
0.9%
86 15
1.2%
87 7
 
0.6%
88 13
1.1%
89 20
1.7%
ValueCountFrequency (%)
159 20
1.7%
158 18
1.5%
157 15
1.2%
156 18
1.5%
155 17
1.4%
154 14
1.2%
153 17
1.4%
152 11
0.9%
151 15
1.2%
150 22
1.8%

Sweating
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
True
836 
False
364 
ValueCountFrequency (%)
True 836
69.7%
False 364
30.3%
2025-01-19T19:13:01.783608image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
True
746 
False
454 
ValueCountFrequency (%)
True 746
62.2%
False 454
37.8%
2025-01-19T19:13:01.879613image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Dizziness
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
True
620 
False
580 
ValueCountFrequency (%)
True 620
51.7%
False 580
48.3%
2025-01-19T19:13:01.975679image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Chest_Pain
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
False
713 
True
487 
ValueCountFrequency (%)
False 713
59.4%
True 487
40.6%
2025-01-19T19:13:02.072684image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Trembling
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
False
610 
True
590 
ValueCountFrequency (%)
False 610
50.8%
True 590
49.2%
2025-01-19T19:13:02.167707image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Medical_History
Categorical

Missing 

Distinct3
Distinct (%)0.3%
Missing122
Missing (%)10.2%
Memory size9.5 KiB
Anxiety
492 
Depression
349 
PTSD
237 

Length

Max length10
Median length7
Mean length7.3116883
Min length4

Characters and Unicode

Total characters7882
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnxiety
2nd rowPTSD
3rd rowDepression
4th rowDepression
5th rowDepression

Common Values

ValueCountFrequency (%)
Anxiety 492
41.0%
Depression 349
29.1%
PTSD 237
19.8%
(Missing) 122
 
10.2%

Length

2025-01-19T19:13:02.295375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-19T19:13:02.417180image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
anxiety 492
45.6%
depression 349
32.4%
ptsd 237
22.0%

Most occurring characters

ValueCountFrequency (%)
e 1190
15.1%
n 841
10.7%
i 841
10.7%
s 698
8.9%
D 586
7.4%
A 492
 
6.2%
x 492
 
6.2%
t 492
 
6.2%
y 492
 
6.2%
p 349
 
4.4%
Other values (5) 1409
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7882
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1190
15.1%
n 841
10.7%
i 841
10.7%
s 698
8.9%
D 586
7.4%
A 492
 
6.2%
x 492
 
6.2%
t 492
 
6.2%
y 492
 
6.2%
p 349
 
4.4%
Other values (5) 1409
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7882
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1190
15.1%
n 841
10.7%
i 841
10.7%
s 698
8.9%
D 586
7.4%
A 492
 
6.2%
x 492
 
6.2%
t 492
 
6.2%
y 492
 
6.2%
p 349
 
4.4%
Other values (5) 1409
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7882
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1190
15.1%
n 841
10.7%
i 841
10.7%
s 698
8.9%
D 586
7.4%
A 492
 
6.2%
x 492
 
6.2%
t 492
 
6.2%
y 492
 
6.2%
p 349
 
4.4%
Other values (5) 1409
17.9%

Medication
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
False
700 
True
500 
ValueCountFrequency (%)
False 700
58.3%
True 500
41.7%
2025-01-19T19:13:02.519677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Caffeine_Intake
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5391667
Minimum0
Maximum5
Zeros199
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-19T19:13:02.828938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7168544
Coefficient of variation (CV)0.67614875
Kurtosis-1.275038
Mean2.5391667
Median Absolute Deviation (MAD)1
Skewness-0.034929421
Sum3047
Variance2.947589
MonotonicityNot monotonic
2025-01-19T19:13:02.935334image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 209
17.4%
4 204
17.0%
3 200
16.7%
0 199
16.6%
2 198
16.5%
1 190
15.8%
ValueCountFrequency (%)
0 199
16.6%
1 190
15.8%
2 198
16.5%
3 200
16.7%
4 204
17.0%
5 209
17.4%
ValueCountFrequency (%)
5 209
17.4%
4 204
17.0%
3 200
16.7%
2 198
16.5%
1 190
15.8%
0 199
16.6%

Exercise_Frequency
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.955
Minimum0
Maximum6
Zeros198
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-19T19:13:03.046999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0615164
Coefficient of variation (CV)0.69763668
Kurtosis-1.3125954
Mean2.955
Median Absolute Deviation (MAD)2
Skewness0.023839904
Sum3546
Variance4.2498499
MonotonicityNot monotonic
2025-01-19T19:13:03.174252image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 198
16.5%
6 182
15.2%
5 170
14.2%
1 170
14.2%
3 162
13.5%
2 162
13.5%
4 156
13.0%
ValueCountFrequency (%)
0 198
16.5%
1 170
14.2%
2 162
13.5%
3 162
13.5%
4 156
13.0%
5 170
14.2%
6 182
15.2%
ValueCountFrequency (%)
6 182
15.2%
5 170
14.2%
4 156
13.0%
3 162
13.5%
2 162
13.5%
1 170
14.2%
0 198
16.5%

Sleep_Hours
Real number (ℝ)

Distinct51
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4815833
Minimum4
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-19T19:13:03.311100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.3
Q15.3
median6.5
Q37.6
95-th percentile8.8
Maximum9
Range5
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.4056252
Coefficient of variation (CV)0.21686449
Kurtosis-1.1395614
Mean6.4815833
Median Absolute Deviation (MAD)1.2
Skewness0.050981698
Sum7777.9
Variance1.9757823
MonotonicityNot monotonic
2025-01-19T19:13:03.475750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8 38
 
3.2%
7.5 33
 
2.8%
6 31
 
2.6%
5.6 31
 
2.6%
7.1 31
 
2.6%
7 30
 
2.5%
8.9 29
 
2.4%
5.9 28
 
2.3%
8.8 28
 
2.3%
6.9 28
 
2.3%
Other values (41) 893
74.4%
ValueCountFrequency (%)
4 10
 
0.8%
4.1 18
1.5%
4.2 27
2.2%
4.3 18
1.5%
4.4 19
1.6%
4.5 19
1.6%
4.6 20
1.7%
4.7 24
2.0%
4.8 38
3.2%
4.9 24
2.0%
ValueCountFrequency (%)
9 4
 
0.3%
8.9 29
2.4%
8.8 28
2.3%
8.7 23
1.9%
8.6 19
1.6%
8.5 24
2.0%
8.4 25
2.1%
8.3 20
1.7%
8.2 22
1.8%
8.1 21
1.8%

Alcohol_Consumption
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4166667
Minimum0
Maximum9
Zeros127
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-19T19:13:03.600734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9025982
Coefficient of variation (CV)0.65719205
Kurtosis-1.2362013
Mean4.4166667
Median Absolute Deviation (MAD)2.5
Skewness0.043116357
Sum5300
Variance8.4250765
MonotonicityNot monotonic
2025-01-19T19:13:03.725083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 132
11.0%
6 129
10.8%
0 127
10.6%
9 125
10.4%
2 121
10.1%
5 120
10.0%
3 117
9.8%
4 113
9.4%
8 112
9.3%
7 104
8.7%
ValueCountFrequency (%)
0 127
10.6%
1 132
11.0%
2 121
10.1%
3 117
9.8%
4 113
9.4%
5 120
10.0%
6 129
10.8%
7 104
8.7%
8 112
9.3%
9 125
10.4%
ValueCountFrequency (%)
9 125
10.4%
8 112
9.3%
7 104
8.7%
6 129
10.8%
5 120
10.0%
4 113
9.4%
3 117
9.8%
2 121
10.1%
1 132
11.0%
0 127
10.6%

Smoking
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
False
875 
True
325 
ValueCountFrequency (%)
False 875
72.9%
True 325
 
27.1%
2025-01-19T19:13:03.842782image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Therapy
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
True
605 
False
595 
ValueCountFrequency (%)
True 605
50.4%
False 595
49.6%
2025-01-19T19:13:03.945442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Panic_Score
Real number (ℝ)

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5691667
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-01-19T19:13:04.050898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7931555
Coefficient of variation (CV)0.50153921
Kurtosis-1.1532939
Mean5.5691667
Median Absolute Deviation (MAD)2
Skewness-0.021647301
Sum6683
Variance7.8017174
MonotonicityNot monotonic
2025-01-19T19:13:04.166324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 139
11.6%
5 130
10.8%
6 128
10.7%
7 123
10.2%
9 123
10.2%
8 120
10.0%
10 113
9.4%
2 111
9.2%
3 108
9.0%
1 105
8.8%
ValueCountFrequency (%)
1 105
8.8%
2 111
9.2%
3 108
9.0%
4 139
11.6%
5 130
10.8%
6 128
10.7%
7 123
10.2%
8 120
10.0%
9 123
10.2%
10 113
9.4%
ValueCountFrequency (%)
10 113
9.4%
9 123
10.2%
8 120
10.0%
7 123
10.2%
6 128
10.7%
5 130
10.8%
4 139
11.6%
3 108
9.0%
2 111
9.2%
1 105
8.8%

Interactions

2025-01-19T19:12:58.466276image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:48.664900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:49.698139image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:50.647145image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:51.618095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:52.706113image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:54.028685image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:55.050195image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:56.139761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:57.200989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:58.562071image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:48.791088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:49.802552image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:50.754781image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:51.731797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:52.812834image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:54.126664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:55.159382image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:56.247780image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:57.297957image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:58.658250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:48.893395image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:49.882186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:50.863373image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:51.834736image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:53.197540image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:54.220679image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:55.270640image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:56.348298image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:57.391561image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:58.755903image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:49.005919image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:49.964656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:50.959274image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:51.952178image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:53.299357image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:54.322666image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:55.384335image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:56.449002image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:57.488692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:58.873966image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:49.118501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:50.065531image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:51.057633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:52.076509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:53.399315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:54.426453image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:55.497545image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:56.578125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:57.601256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:58.969023image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:49.228590image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:50.167309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:51.155711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:52.186487image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:53.508525image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:54.536413image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:55.616637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:56.691917image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:57.719388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:59.065573image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:49.330858image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:50.256414image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:51.245021image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:52.298281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:53.602120image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:54.638208image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:55.721151image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:56.788640image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:57.822042image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:59.157693image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:49.420314image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:50.348518image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:51.345995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:52.407344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:53.708704image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:54.744328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:55.828198image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:56.885092image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:57.923105image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:59.245964image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:49.516314image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:50.433779image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:51.440715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:52.516271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:53.821866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:54.847399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:55.930887image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:56.981261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:58.015108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:59.330365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:49.605313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:50.529729image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:51.528996image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:52.599555image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:53.921990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:54.940962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:56.039007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:57.076727image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T19:12:58.093836image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-19T19:13:04.271105image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
AgeAlcohol_ConsumptionCaffeine_IntakeChest_PainDizzinessDuration_MinutesExercise_FrequencyGenderHeart_RateIDMedical_HistoryMedicationPanic_Attack_FrequencyPanic_ScoreShortness_of_BreathSleep_HoursSmokingSweatingTherapyTremblingTrigger
Age1.0000.0280.0020.0000.0000.0510.0130.000-0.0030.0020.0410.000-0.016-0.0090.0000.0410.0000.0980.0000.0650.000
Alcohol_Consumption0.0281.000-0.0100.0210.051-0.0210.0090.0000.0350.0050.0580.000-0.0190.0370.0000.0710.0460.0000.0000.0000.000
Caffeine_Intake0.002-0.0101.0000.0000.000-0.0320.0070.0000.0090.0520.0490.0000.001-0.0090.0290.0170.0000.0450.0000.0000.027
Chest_Pain0.0000.0210.0001.0000.0000.0000.0000.0400.0000.0000.0510.0000.0000.0700.0000.0780.0000.0440.0000.0000.054
Dizziness0.0000.0510.0000.0001.0000.0000.0000.0250.0000.0330.0120.0000.0000.0640.0000.0000.0000.0000.0000.0000.069
Duration_Minutes0.051-0.021-0.0320.0000.0001.000-0.0120.000-0.0010.0140.0000.000-0.0460.0040.071-0.0100.0000.0000.0000.0000.000
Exercise_Frequency0.0130.0090.0070.0000.000-0.0121.0000.000-0.001-0.0420.0650.0970.0000.0030.0650.0110.0000.0000.0000.0720.000
Gender0.0000.0000.0000.0400.0250.0000.0001.0000.0000.0000.0000.0000.0260.0570.0000.0370.0000.0360.0000.0000.000
Heart_Rate-0.0030.0350.0090.0000.000-0.001-0.0010.0001.0000.0220.0000.0590.035-0.0060.048-0.0040.0000.0000.0000.0390.000
ID0.0020.0050.0520.0000.0330.014-0.0420.0000.0221.0000.0000.0000.011-0.0100.0000.0150.0260.0000.0000.0350.037
Medical_History0.0410.0580.0490.0510.0120.0000.0650.0000.0000.0001.0000.0170.0300.0170.0660.0000.0000.0000.0000.0360.000
Medication0.0000.0000.0000.0000.0000.0000.0970.0000.0590.0000.0171.0000.0660.0490.0000.0000.0000.0000.0030.0320.065
Panic_Attack_Frequency-0.016-0.0190.0010.0000.000-0.0460.0000.0260.0350.0110.0300.0661.000-0.0050.068-0.0060.0000.0000.0230.0460.000
Panic_Score-0.0090.037-0.0090.0700.0640.0040.0030.057-0.006-0.0100.0170.049-0.0051.0000.0000.0170.0470.0650.0000.0610.017
Shortness_of_Breath0.0000.0000.0290.0000.0000.0710.0650.0000.0480.0000.0660.0000.0680.0001.0000.0690.0000.0000.0160.0000.047
Sleep_Hours0.0410.0710.0170.0780.000-0.0100.0110.037-0.0040.0150.0000.000-0.0060.0170.0691.0000.0000.0000.0530.0660.000
Smoking0.0000.0460.0000.0000.0000.0000.0000.0000.0000.0260.0000.0000.0000.0470.0000.0001.0000.0500.0000.0000.000
Sweating0.0980.0000.0450.0440.0000.0000.0000.0360.0000.0000.0000.0000.0000.0650.0000.0000.0501.0000.0000.0000.095
Therapy0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0230.0000.0160.0530.0000.0001.0000.0080.000
Trembling0.0650.0000.0000.0000.0000.0000.0720.0000.0390.0350.0360.0320.0460.0610.0000.0660.0000.0000.0081.0000.000
Trigger0.0000.0000.0270.0540.0690.0000.0000.0000.0000.0370.0000.0650.0000.0170.0470.0000.0000.0950.0000.0001.000

Missing values

2025-01-19T19:12:59.483631image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-19T19:12:59.682190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDAgeGenderPanic_Attack_FrequencyDuration_MinutesTriggerHeart_RateSweatingShortness_of_BreathDizzinessChest_PainTremblingMedical_HistoryMedicationCaffeine_IntakeExercise_FrequencySleep_HoursAlcohol_ConsumptionSmokingTherapyPanic_Score
0156Female95Caffeine134YesNoYesYesNoAnxietyNo236.45YesYes5
1246Male89Stress139YesYesNoNoNoPTSDYes255.03NoYes7
2332Female631PTSD141NoYesYesNoNoDepressionNo408.38NoYes7
3460Male520Caffeine109YesYesNoNoYesDepressionNo335.38NoNo1
4525Non-binary610Caffeine101YesNoYesYesYesDepressionNo367.22NoNo5
5638Male044Social Anxiety154YesYesYesNoNoDepressionYes144.86NoNo8
6756Male017PTSD108YesYesYesNoNoAnxietyNo066.04YesYes7
7836Male739PTSD120YesNoNoYesYesPTSDNo048.58NoYes2
8940Non-binary123Unknown121YesNoNoYesNoAnxietyNo454.81NoYes8
91028Female916Caffeine144NoNoYesNoYesAnxietyNo467.32NoYes2
IDAgeGenderPanic_Attack_FrequencyDuration_MinutesTriggerHeart_RateSweatingShortness_of_BreathDizzinessChest_PainTremblingMedical_HistoryMedicationCaffeine_IntakeExercise_FrequencySleep_HoursAlcohol_ConsumptionSmokingTherapyPanic_Score
1190119160Female330PTSD107YesYesYesYesNoDepressionNo547.42YesYes1
1191119261Male731Phobia156NoYesYesNoYesNaNNo328.89NoNo4
1192119341Female231Unknown80YesNoNoYesNoNaNYes036.12NoNo3
1193119442Female519Stress139YesNoYesNoYesAnxietyNo054.44YesNo2
1194119524Non-binary812Unknown135YesYesNoNoYesPTSDYes444.63NoNo2
1195119623Female011Stress114YesYesYesNoYesDepressionYes537.95NoYes8
1196119741Male044Stress109YesNoNoNoYesAnxietyYes434.37NoYes1
1197119850Male716Phobia133NoYesYesNoNoDepressionYes108.57NoYes8
1198119946Male217Unknown115YesYesNoNoYesAnxietyNo455.43YesYes9
1199120060Male421Stress134YesYesNoNoYesDepressionYes348.50NoNo6